Frequency Estimation

Frequency Format

A frequency format is one way to represent a probabilistic statement. Other formats commonly used to represent the likelihood of an event are a percentage format (with a range of 0–100%) and a probability format (with a range of 0.0–1.0). Frequency estimation requires consideration of both a numerator (the anticipated number of times the event will occur) and a denominator (the total number of times at risk for the event to occur). Representing risk in a frequency format may be a more intuitive way to communicate risk information for certain types of judgment tasks than using other probability formats.

Needed Skills

Accurate frequency estimation requires some knowledge about the outcome being estimated and the ability to understand probabilistic information. Accurate frequency estimation also requires skills in numeracy, including a conceptual understanding of the concepts of probability. People are often inaccurate in frequency estimates of the likelihood of their developing or dying from a given disease or the benefit of a given treatment. For example, women tend to overestimate their personal risk of dying from breast cancer. In contrast, smokers tend to underestimate their risk of dying from lung cancer.

Types of Reasoning Used

There are two general types of reasoning used in frequency estimation: deliberative reasoning and experiential reasoning. In deliberative reasoning, people will attempt to integrate knowledge of relevant probabilities in formulating an estimation of [Page 512]frequency. In experiential reasoning, people will rely to a greater degree on intuition, emotion, and affect in formulating an estimate of frequency. One aspect of experiential reasoning is use of the availability heuristic. The availability heuristic incorporates personal experience and exposure to the outcome in question in making a frequency estimate. The use of a pictograph with a spatial array to convey frequency information has been found to decrease the bias that can be associated with anecdotal information presented alongside frequency information in the context of a medical decision. Frequency estimates may also be influenced by optimistic bias, which reflects people's tendency to view themselves as being at lower risk than others. One theory that explains how people formulate frequency estimates is fuzzy-trace theory. Fuzzy-trace theory holds that people will naturally conceptualize frequency estimates in the most general way possible in order to solve a problem or make a decision.

Importance of Response Scale and Format

Numeric estimates of frequency are influenced by additional factors including the magnitude of the risk assessed, the response scale used, and whether the frequency estimate is made in isolation or in comparison with other risks. There is a tendency to overestimate small-frequency occurrences and to underestimate large-frequency occurrences. One approach to assist people with estimates of small frequencies is the use of a scale that has a “magnifying glass” to represent probabilities between 0% and 1% on a logarithmic scale or to use other response scales with greater discrimination among smaller probabilities. The choice of response scale can influence the magnitude of the frequency estimates assessed. Specifically, frequency estimates have been found to differ when using a percentage versus frequency format scale. Frequency estimation can also be assessed using a scale with a 1/X format, with an increasing value of X indicating a lower frequency. However, the 1/X format has been found to be a more difficult format for judgment tasks in which a person is asked to compare risk magnitudes. In frequency judgments, people may find the task easier and be more accurate when comparing their risk with that of others versus providing a frequency estimate for their risk of a given outcome in isolation.

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